Roxana Daneshjou, MD

Abstract

Artificial intelligence (AI)-based applications have the potential to improve the quality and efficiency of patient care in dermatology. Unique challenges in the development and validation of these technologies may limit their generalizability and real-world applicability. Before widespread adoption of AI interventions, randomized trials should be conducted to evaluate their efficacy, safety, and cost effectiveness in clinical settings. The recent SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - AI extension) and CONSORT-AI (Consolidated Standards of Reporting Trials - AI extension) guidance provide recommendations for reporting the methods and results of trials involving AI interventions. High-quality trials will provide gold standard evidence to support the adoption of AI for the benefit of patient care.

View details for DOI 10.1016/j.jid.2021.02.744

View details for PubMedID 33766511

Abstract

The large volume of data used in cancer diagnosis presents a unique opportunity for deep learning algorithms, which improve in predictive performance with increasing data. When applying deep learning to cancer diagnosis, the goal is often to learn how to classify an input sample (such as images or biomarkers) into predefined categories (such as benign or cancerous). In this article, we examine examples of how deep learning algorithms have been implemented to make predictions related to cancer diagnosis using clinical, radiological, and pathological image data. We present a systematic approach for evaluating the development and application of clinical deep learning algorithms. Based on these examples and the current state of deep learning in medicine, we discuss the future possibilities in this space and outline a roadmap for implementations of deep learning in cancer diagnosis.

View details for DOI 10.1016/j.bbcan.2021.188515

View details for PubMedID 33513392

Abstract

Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.

View details for PubMedID 33691019

View details for DOI 10.1016/j.jdcr.2020.07.009

View details for PubMedID 32835046

View details for DOI 10.1001/jamadermatol.2020.0315

View details for PubMedID 32186655

Abstract

Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility.

View details for DOI 10.1038/s41467-020-14594-5

View details for PubMedID 32041948

Abstract

Machine learning and deep learning have revolutionized our ability to analyze and find patterns in multi-dimensional and intricate datasets. As such, these methods have the ability to help us decipher the large volume of data generated within healthcare. These tools hold the promise of enhancing patient care through several modalities, including clinical decision support, monitoring tools, image interpretation, and triaging capabilities. For the 2020 Pacific Symposium on Biocomputing's session on Artificial Intelligence for Enhancing Clinical Medicine, we highlight novel research on the application of artificial intelligence to solve problems within the field of medicine.

View details for PubMedID 33381619

View details for DOI 10.1016/j.jid.2020.08.005

View details for PubMedID 32972520

View details for DOI 10.1016/j.jaad.2020.07.038

View details for PubMedID 32682885

Abstract

Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent due to differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an AUC of 0.65. Here we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. Additionally, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans. This article is protected by copyright. All rights reserved.

View details for DOI 10.1002/humu.23825

View details for PubMedID 31140652

Abstract

Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.

View details for DOI 10.12788/j.sder.2019.009

View details for PubMedID 31051019

Abstract

The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.

View details for PubMedID 29635477

View details for DOI 10.1093/hmg/ddy116

View details for Web of Science ID 000431884200012

Abstract

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

View details for DOI 10.1002/humu.23280

View details for Web of Science ID 000407861100014

View details for PubMedID 28634997

View details for PubMedCentralID PMC5600620

Abstract

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

View details for PubMedID 29178968

View details for DOI 10.1056/NEJMsr1406261

View details for PubMedID 26014595

View details for DOI 10.1097/FPC.0000000000000100

View details for Web of Science ID 000346632900006

View details for PubMedID 25370453

View details for PubMedCentralID PMC4261059

Abstract

The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes ( 35 and 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.

View details for DOI 10.1182/blood-2014-04-568436

View details for PubMedID 25079360

Abstract

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.

View details for DOI 10.1371/journal.pgen.1004704

View details for Web of Science ID 000344650700067

View details for PubMedCentralID PMC4191946

Abstract

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.

View details for DOI 10.1371/journal.pgen.1004704

View details for PubMedID 25299611

View details for PubMedCentralID PMC4191946

View details for Web of Science ID 000335405200021

View details for PubMedID 24804303

Abstract

The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

View details for PubMedID 24297550

View details for Web of Science ID 000461865200022

Abstract

BACKGROUND: VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. METHODS: We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged 18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 -1639GA, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<510(-8) in the discovery cohort and p<00038 in the replication cohort. FINDINGS: The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=15110(-8)). This association was confirmed in the replication cohort (p=50410(-5)); analysis of the two cohorts together produced a p value of 4510(-12). Individuals heterozygous for the rs12777823 A allele need a dose reduction of 692 mg/week and those homozygous 934 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). INTERPRETATION: A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. FUNDING: National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

View details for DOI 10.1016/S0140-6736(13)60681-9

View details for PubMedID 23755828

Abstract

Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

View details for DOI 10.1186/1471-2164-14-S3-S11

View details for Web of Science ID 000319869500011

View details for PubMedID 23819817

Abstract

Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

View details for DOI 10.1186/1471-2164-14-S3-S11

View details for PubMedID 23819817

Abstract

There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.

View details for DOI 10.1371/journal.pcbi.1002817

View details for Web of Science ID 000312901500023

View details for PubMedID 23300409

View details for PubMedCentralID PMC3531317

Abstract

Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

View details for DOI 10.1126/scitranslmed.3003377

View details for Web of Science ID 000301538300005

View details for PubMedID 22422992

View details for PubMedCentralID PMC3382018

Abstract

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.russ.altman@stanford.edu

View details for DOI 10.1093/bioinformatics/btr295

View details for Web of Science ID 000291752600050

View details for PubMedID 21596790

View details for PubMedCentralID PMC3117361